Diffusion Tensor Imaging Techniques Research Articles

Role of Microstructural White Matter Changes of Somatosensory Cortex in Stress Among Non-Clinical Population: A Diffusion Tensor Imaging Study

Introduction: Stress is a common response to stressors or potential threats, resulting in physical, affective, and cognitive changes. Emotions are linked to interpretations of bodily alterations, with the somatosensory cortex, including the postcentral gyrus, playing a key role in emotional processing. This study aimed to investigate the relationship between stress and microstructural white matter changes in the somatosensory cortex among healthy adults, crucial for early detection and treatment of stress in non-clinical populations. Materials and Methods: A total of 30 participants were recruited. The participants were administered the Depression, Anxiety, and Stress Scale 21 (DASS-21) questionnaire. All subjects underwent MRI brain scanning, with diffusion tensor imaging (DTI) used to assess white matter integrity. The association between stress scores in DASS-21 and DTI parameters was analyzed. Results: A significant negative correlation between stress scores and fractional anisotropy (FA) values in the left postcentral gyrus (r = -0.393, p = 0.32) was found, indicating early impairment by stress. No significant correlation was found in the right postcentral gyrus (r = -0.300, p = 0.107). Conclusion: Our research results demonstrated that the microstructural somatosensory cortex, especially the left postcentral gyrus, might have early changes impairment by stress using the technique of diffusion tensor imaging (DTI). Therefore, the microstructural white matter changes in the brain before suffering the disorder may be crucial and can be a novel potential biomarker for the early detection and intervention of the disease in the non-clinical population.

Correspondence between white matter hyperintensities and regional grey matter volumes in Alzheimer's disease.

White matter hyperintensities (WMH) are the most common neuroimaging manifestation of cerebral small vessel disease, and is frequently observed in Alzheimer's disease (AD). This study aimed to investigate the relationship between WMH and cognition and to verify the mediation of grey matter atrophy in this relationship. The diffusion tensor imaging (DTI) technique analyses white matter fiber tract to assess white matter integrity. Voxel-based morphometry was applied to measure the grey matter volume (GMV). A linear regression model was applied to examine the associations between WMH and GMV, and mediation analyses was performed to determine the mediating role of regional GMV in the effect of WMH on cognitive function. Compared to the HC group, AD group have 8 fiber tract fractional anisotropy (FA) decreased and 16 fiber tract mean diffusivity (MD) increased. Compared to AD without WMH, AD with high WMH had 9 fiber tracts FA decreased and 13 fiber tracts MD increased. High WMH volume was negatively correlated with GMV in the frontal-parietal region. Low WMH volume was also negatively correlated with GMV except for the three regions (right angular gyrus, right superior frontal gyrus and right middle/inferior parietal gyrus), where GMV was positively correlated. Mediation analysis showed that the association between WMH and executive function or episodic memory were mediated by GMV in the frontal-parietal region. Damage to white matter integrity was more severe in AD with WMH. Differential changes in DTI metrics may be caused by progressive myelin and axonal damage. There was a negative correlation between WMH and grey matter atrophy in frontal-parietal regions in a volume-dependent manner. This study indicates the correspondence between WMH volume and GMV in cognition, and GMV being a key modulator between WMH and cognition in AD. This result will contribute to understanding the progression of the disease process and applying targeted therapeutic intervention in the earlier stage to delay neurodegenerative changes in frontal-parietal regions to achieve better treatment outcomes and affordability.

Diffusion Tensor Imaging: Techniques and Applications for Peripheral Nerve Injury.

Peripheral nerve injuries (PNIs) represent a complex clinical challenge, necessitating precise diagnostic approaches for optimal management. Traditional diagnostic methods often fall short in accurately assessing nerve recovery as these methods rely on the completion of nerve reinnervation, which can prolong a patient's treatment. Diffusion tensor imaging (DTI), a noninvasive magnetic resonance imaging (MRI) technique, has emerged as a promising tool in this context. DTI offers unique advantages including the ability to quantify nerve recovery and provide in vivo visualizations of neuronal architecture. Therefore, this review aims to examine and outline DTI techniques and its utility in detecting distal nerve regeneration in both preclinical and clinical settings for peripheral nerve injury.

White matter structure and derived network properties are used to predict the progression from mild cognitive impairment of older adults to Alzheimer’s disease

ObjectiveTo identify white matter fiber injury and network changes that may lead to mild cognitive impairment (MCI) progression, then a joint model was constructed based on neuropsychological scales to predict high-risk individuals for Alzheimer's disease (AD) progression among older adults with MCI.MethodsA total of 173 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database and randomly divided into training and testing cohorts. Forty-five progressed to AD during a 4-year follow-up period. Diffusion tensor imaging (DTI) techniques extracted relevant DTI quantitative features for each patient. In addition, brain networks were constructed based on white matter fiber bundles to extract network property features. Ensemble dimensionality reduction was applied to reduce both DTI quantitative features and network features from the training cohort, and machine learning algorithms were added to construct white matter signature. In addition, 52 patients from the National Alzheimer's Coordinating Center (NACC) database were used for external validation of white matter signature. A joint model was subsequently generated by combining with scale scores, and its performance was evaluated using data from the testing cohort.ResultsBased on multivariate logistic regression, clinical dementia rating and Alzheimer’s disease assessment scales (CDRS and ADAS, respectively) were selected as independent predictive factors. A joint model was constructed in combination with the white matter signature. The AUC, sensitivity, and specificity in the training cohort were 0.938, 0.937, and 0.91, respectively, and the AUC, sensitivity, and specificity in the test cohort were 0.905, 0.923, and 0.872, respectively. The Delong test showed a statistically significant difference between the joint model and CDRS or ADAS scores (P < 0.05), yet no significant difference between the joint model and the white matter signature (P = 0.341).ConclusionThe present results demonstrate that a joint model combining neuropsychological scales can be constructed by using machine learning and DTI technology to identify MCI patients who are at high-risk of progressing to AD.

Microscopic fractional anisotropy outperforms multiple sclerosis lesion assessment and clinical outcome associations over standard fractional anisotropy tensor.

We aimed to compare the ability of diffusion tensor imaging and multi-compartment spherical mean technique to detect focal tissue damage and in distinguishing between different connectivity patterns associated with varying clinical outcomes in multiple sclerosis (MS). Seventy-six people diagnosed with MS were scanned using a SIEMENS Prisma Fit 3T magnetic resonance imaging (MRI), employing both conventional (T1w and fluid-attenuated inversion recovery) and advanced diffusion MRI sequences from which fractional anisotropy (FA) and microscopic FA (μFA) maps were generated. Using automated fiber quantification (AFQ), we assessed diffusion profiles across multiple white matter (WM) pathways to measure the sensitivity of anisotropy diffusion metrics in detecting localized tissue damage. In parallel, we analyzed structural brain connectivity in a specific patient cohort to fully grasp its relationships with cognitive and physical clinical outcomes. This evaluation comprehensively considered different patient categories, including cognitively preserved (CP), mild cognitive deficits (MCD), and cognitively impaired (CI) for cognitive assessment, as well as groups distinguished by physical impact: those with mild disability (Expanded Disability Status Scale [EDSS] <=3) and those with moderate-severe disability (EDSS >3). In our initial objective, we employed Ridge regression to forecast the presence of focal MS lesions, comparing the performance of μFA and FA. μFA exhibited a stronger association with tissue damage and a higher predictive precision for focal MS lesions across the tracts, achieving an R-squared value of .57, significantly outperforming the R-squared value of .24 for FA (p-value <.001). In structural connectivity, μFA exhibited more pronounced differences than FA in response to alteration in both cognitive and physical clinical scores in terms of effect size and number of connections. Regarding cognitive groups, FA differences between CP and MCD groups were limited to 0.5% of connections, mainly around the thalamus, while μFA revealed changes in 2.5% of connections. In the CP and CI group comparison, which have noticeable cognitive differences, the disparity was 5.6% for FA values and 32.5% for μFA. Similarly, μFA outperformed FA in detecting WM changes between the MCD and CI groups, with 5% versus 0.3% of connections, respectively. When analyzing structural connectivity between physical disability groups, μFA still demonstrated superior performance over FA, disclosing a 2.1% difference in connectivity between regions closely associated with physical disability in MS. In contrast, FA spotted a few regions, comprising only 0.6% of total connections. In summary, μFA emerged as a more effective tool than FA in predicting MS lesions and identifying structural changes across patients with different degrees of cognitive and global disability, offering deeper insights into the complexities of MS-related impairments.

Investigating white matter changes in auditory cortex and association fibres related to speech processing in noise-induced hearing loss: a diffusion tensor imaging study

BackgroundThis study explores the impact of noise-induced hearing loss (NIHL) on the microstructural integrity of white matter tracts in the brain, focusing on areas involved in speech processing. While the primary impact of hearing loss occurs in the inner ear, these changes can extend to the central auditory pathways and have broader effects on brain function. Our research aimed to uncover the neural mechanisms underlying hearing loss-related deficits in speech perception and cognition among NIHL patients.MethodsThe study included two groups: nine bilateral NIHL patients and nine individuals with normal hearing. Advanced diffusion tensor imaging techniques were employed to assess changes in the white matter tracts. Regions of interest (ROIs), including the auditory cortex, cingulum, arcuate fasciculus, and longitudinal fasciculus, were examined. Fractional anisotropy (FA) values from these ROIs were extracted for analysis.ResultsOur findings indicated significant reductions in FA values in NIHL patients, particularly in the left cingulum, right cingulum, and left inferior longitudinal fasciculus. Notably, no significant changes were observed in the auditory cortex, arcuate fasciculus, superior longitudinal fasciculus, middle longitudinal fasciculus, and right inferior longitudinal fasciculus, suggesting differential impacts of NIHL on various white matter tracts.ConclusionsThe study's findings highlight the importance of considering association fibres related to speech processing in treating NIHL, as the broader neural network beyond primary auditory structures is significantly impacted. This research contributes to understanding the neurological impact of NIHL and underscores the need for comprehensive approaches in addressing this condition.

Combined diffusion tensor imaging and quantitative susceptibility mapping to characterize normal-appearing white matter in self-limited epilepsy with centrotemporal spikes.

In brain development, Myelination is the characteristic feature of white matter maturation, which plays an important role in efficient information transmitting. The white matter abnormality has been reported to be associated with self-limited epilepsy with centrotemporal spikes (SeLECTS). This study aimed to detect the altered white matter region in the SeLECTS patients by the combination of diffusion tensor imaging (DTI) and quantitative susceptibility mapping (QSM) technique. 27 children with SeLECTS and 23 age- and gender-matched healthy children were enrolled. All participants were scanned with 3.0-T MRI to acquire the structure, diffusion and susceptibility-weighted data. The susceptibility and diffusion weighted data were processed to obtain quantitative susceptibility map and fraction anisotropy (FA) map. Then voxel-wise tract-based spatial statistics (TBSS) were used to analyze quantitative susceptibility and FA data. Both DTI and QSM revealed extensive white matter alterations in the frontal, parietal, and temporal lobes in SeLECTS patients. The overlapped region of DTI and QSM analyses was located in the fiber tracts of the corona radiata. The FA values in this overlapped region were negatively correlated with the magnetic susceptibility values. Our results suggest that TBSS-based QSM can be employed as a novel approach for characterizing alterations in white matter in SeLECTS. And the combination of QSM and DTI can provide a more comprehensive evaluation of white matter integrity by utilizing different biophysical features.

High angular resolution diffusion imaging (HARDI) of porcine menisci: a comparison of diffusion tensor imaging and generalized q-sampling imaging.

Diffusion magnetic resonance imaging (MRI) allows for the quantification of water diffusion properties in soft tissues. The goal of this study was to characterize the 3D collagen fiber network in the porcine meniscus using high angular resolution diffusion imaging (HARDI) acquisition with both diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI). Porcine menisci (n=7) were scanned ex vivo using a three-dimensional (3D) HARDI spin-echo pulse sequence with an isotropic resolution of 500 µm at 7.0 Tesla. Both DTI and GQI reconstruction techniques were used to quantify the collagen fiber alignment and visualize the complex collagen network of the meniscus. The MRI findings were validated with conventional histology. DTI and GQI exhibited distinct fiber orientation maps in the meniscus using the same HARDI acquisition. We found that crossing fibers were only resolved with GQI, demonstrating the advantage of GQI over DTI to visualize the complex collagen fiber orientation in the meniscus. Furthermore, the MRI findings were consistent with conventional histology. HARDI acquisition with GQI reconstruction more accurately resolves the complex 3D collagen architecture of the meniscus compared to DTI reconstruction. In the future, these technologies have the potential to nondestructively assess both normal and abnormal meniscal structure.

Role of diffusion tensor imaging in stenotic and non-stenotic spinal canal

Background and purposeCervical Spondylotic Myelopathy (CSM) is a gradually escalating spinal cord disturbance set in motion by the degenerative narrowing of the vertebral canal. Routine MRI may fail to detect the subtle early alterations of the cord. MRI Diffusion Tensor Imaging (DTI) possesses the potential to detect these changes. This study intends to estimate the potential of the DTI technique in non-stenotic & stenotic spinal canals in individuals affected with CSM. MethodologySixty-four subjects who met the requirements of the inclusion criteria were incorporated into the investigation. All subjects underwent routine MRI sequences in addition to DTI of the cervical spine region. Scalars such as Fractional Anisotropy (FA), besides Apparent Diffusion Coefficient (ADC), were computed at each cervical intervertebral fibrocartilaginous disc level for all subjects. DTI fiber tractography was then performed to qualitatively assess the microstructural integrity of the tracts. ResultsA noteworthy difference (p<0.05) was seen in the FA parameter and ADC parameter values between the stenotic and non-stenotic groups, with the non-stenotic group having a higher mean FA and a lower ADC than the stenotic group (at the level of stenosis). A significant difference in age was seen between both groups, with most of the patients in the stenotic group belonging to 40 years and above. Tractography helped in demonstrating the morphology of the fiber tracts. ConclusionDTI parameters, namely FA and ADC, are sensitive to damage to the white matter and can be used to detect microstructural changes in the cord. However, standardization of the protocol is necessary when imaging the spinal canal.

Diffusion tensor imaging techniques show that parkin gene S/N167 polymorphism is responsible for extensive brain white matter damage in patients with Parkinson's disease

ObjectiveTo explore the influence of disease and genetic factors on the white matter microstructure in patients with PD. The white matter microstructural changes in the substantia nigra-striatum system were detected by diffusion tensor imaging (DTI) using the region of interest (ROI) and diffusion tensor tracer (DTT) methods. MethodsPatients with primary Parkinson's disease (PD) without a family history of PD were selected and divided into PD-G/G and PD-G/A groups according to their parkin S/N167 polymorphism. Control groups matched for age, sex, and gene type (G/G and G/A) were also included. Three-dimensional brain volume imaging (3D-BRAVO) and DTI were performed. The microstructural changes in the substantia nigra-striatum system were evaluated by the ROI and DTT methods. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Hoehn-Yahr (H–Y) staging, and the third part of the Unified Parkinson's Disease Rating (UPDRS-III) scales evaluated the cognitive and motor function impairment in patients with PD. Independent samples t-test compared normally-distributed data, and the Wilcoxon rank sum test compared measurement or categorical non-normally distributed data. Multiple regression analysis was used to analyze the correlation between various DTI indicators and the MMSE, MoCA, UPDRS-III, and H–Y scores in the PD-G/G and PD-G/A groups. P < 0.05 was considered statistically significant. ResultsThe white matter microstructural changes in the nigrostriatal pathway differed significantly between the PD or PD-G/A and the control group (P < 0.05)The ROI method showed that the left globus pallidus radial diffusivity (RD) value was negatively correlated with the MMSE score (r = −0.404, P = 0.040), and the left substantia nigra (LSN) fractional anisotropy (FA) value was positively correlated with the MoCA score (r = 0.405, P = 0.040) and negatively with the H–Y stage (r = −0.479, P = 0.013).The DTT method showed that the MMSE score was positively correlated with the right substantia nigra (RSN) FA value (r = 0.592, P = 0.001) and negatively with its RD value (r = −0.439, P = 0.025). The H–Y grade was negatively correlated with the number of fibers in the RSN (r = −0.406, P = 0.040). The UPDRS-Ⅲ score was positively correlated with the mean diffusivity (r = 0.420, P = 0.033) and RD (r = 0.396, P = 0.045) values of the LSN, and the AD value of the RSN (r = 0.439, P = 0.025). ConclusionThe DTI technique detected extensive white matter fiber damage in patients with PD, primarily in those with the G/A genotype, that led to motor and cognitivesymptoms.

Learning Spectral Fractional Anisotropy and Mean Diffusivity Features as Neuroimaging Biomarkers for Tracking White Matter Integrity Changes in Myotonic Dystrophy Type 1 Patients using Deep Convolutional Neural Networks.

Myotonic dystrophy type 1 (DM1) is a genetic neuromuscular progressive multisystem disease that results in a broad spectrum of clinical central nervous system (CNS) involvement, including problems with memory, attention, executive functioning, and social cognition. Fractional anisotropy and mean diffusivity along-tract data calculated using diffusion tensor imaging techniques play a vital role in assessing white matter microstructural changes associated with neurodegeneration caused by DM1. In this work, a novel spectrogram-based deep learning method is proposed to characterize white matter network alterations in DM1 with the goal of building a deep learning model as neuroimaging biomarkers of DM1. The proposed method is evaluated on fractional anisotropies and mean diffusivities along-tract data calculated for 25 major white matter tracts of 46 DM1 patients and 96 unaffected controls. The evaluation data consists of a total of 7100 spectrogram images. The model achieved 91% accuracy in identifying DM1, a significant improvement compared to previous methods.Clinical relevance- Clinical care of DM1 is particularly challenging due to DM1 multisystem involvement and the disease variability. Patients with DM1 often experience neurological and psychological symptoms, such as excessive sleepiness and apathy, that greatly impact their quality of life. Some of DM1 CNS symptoms may be responsive to treatment. The goal of this research is to gain a deeper understanding of the impact of DM1 on the CNS and to develop a deep learning model that can serve as a biomarker for the disease, with the potential to be used in future clinical trials as an outcome measure.

Brain alterations in ovariohysterectomized rats revealed by diffusion tensor imaging.

Women undergoing hysterectomy with oophorectomy have an increased risk of Alzheimer's disease and Parkinson's disease. However, postoperative neuroimaging data on pathogenic processes in the brain are limited. The aim of this study was to investigate the potential effect of ovariohysterectomy on brain integrity in rat model using diffusion tensor imaging (DTI) technique for the first time. We enrolled 13 rats each in the control and ovariohysterectomy groups. Rats in the ovariohysterectomy group underwent the ovariohysterectomy at 7 weeks of age, and all rats underwent DTI scans at 9 weeks of age. The DTI-derived parameters, such as fractional anisotropy and mean diffusivity, were compared between the control and ovariohysterectomy groups. Compared to the control group, the ovariohysterectomy group showed significantly lower fractional anisotropy in various brain regions, including the corpus callosum, bilateral striatum, and bilateral cortex (all P < 0.05), suggesting neuronal injury in ovariohysterectomized rats. Mean diffusivity did not differ significantly between groups (all P > 0.05). Rats undergoing ovariohysterectomy had lower fractional anisotropy compared to control in widespread brain regions, suggesting neuronal injury and demyelination. Therefore, neuroimaging should be performed to monitor brain alterations in women after hysterectomy with bilateral oophorectomy in clinical settings.

Application study of DTI combined with ASL in the crossed cerebellar diaschisis after subacute cerebral hemorrhage.

To study the value of 3.0T magnetic resonance imaging with diffusion tensor imaging (DTI) and 3D-arterial spin labeling (ASL) perfusion imaging in the diagnosis of the crossed cerebellar diaschisis (CCD) after the unilateral supratentorial subacute cerebral hemorrhage. Fifty-eight patients with the unilateral supratentorial subacute cerebral hemorrhage who underwent diffusion tensor imaging (DTI), 3D-arterial spin labeling (ASL), and conventional magnetic resonance imaging (MRI) scanning were enrolled. Cerebral blood flow (CBF) values of the perihematomal edema (PHE) and bilateral cerebellar hemisphere were measured on ASL mapping, and the fractional anisotropy (FA) and mean diffusivity (MD) values of the bilateral cortical, pontine, and middle cerebellar peduncle (MCP) were measured on DTI mapping. In the CCD(+) group, FA values of the cerebral cortex and pontine ipsilateral to the lesion were statistically reduced compared to the contralateral side (P < 0.05), and the FA and MD values of the middle cerebellar peduncle (MCP) contralateral to the lesion were statistically reduced compared to the ipsilateral side (P < 0.05). Positive correlation was detected between the CBF values of the perihematomal edema (PHE) and the CBF values of cerebellar hemispheres (r = 0.642, P < 0.05), whereas the CBF values of PHE had a significantly high positive correlation with the FA in the contralateral MCP (r = 0.854, P < 0.05). CBF values in the contralateral cerebellar hemisphere correlated with FA (r = 0.466, P < 0.05) and MD values (r = 0.718, P < 0.05) in the contralateral MCP. Hemodynamic alterations of PHE and cortical-ponts-cerebellum (CPC) fibrous pathway damage are associated with the development of CCD; DTI technique can assess the degree of CPC fiber pathway injury at an early stage.

Comparative analysis of freezing of gait in distinct Parkinsonism types by diffusion tensor imaging method and cognitive profiles.

Freezing of gait (FOG) is an episodic gait pattern that is common in advanced Parkinson's disease (PD) and other atypical parkinsonism syndromes. Recently, disturbances in the pedunculopontine nucleus (PPN) and its connections have been suggested to play a critical role in the development of FOG. In this study, we aimed to demonstrate possible disturbances in PPN and its connections by performing the diffusion tensor imaging (DTI) technique. We included 18 patients of PD with FOG [PD-FOG], 13 patients of PD without FOG [PD-nFOG] and 12 healthy subjects as well as a group of patients with progressive supranuclear palsy (PSP), an atypical parkinsonism syndrome which is very often complicated with FOG [6 PSP-FOG, 5 PSP-nFOG]. To determine the specific cognitive parameters that can be related to FOG, deliberate neurophysiological evaluations of all the individuals were performed. The comparative analyses and correlation analyses were performed to reveal the neurophysiological and DTI correlates of FOG in either group. We have found disturbances in values reflecting microstructural integrity of the bilateral superior frontal gyrus (SFG), bilateral fastigial nucleus (FN), left pre-supplementary motor area (SMA) in the PD-FOG group relative to the PD-nFOG group. The analysis of the PSP group also demonstrated disturbance in left pre-SMA values in the PSP-FOG group likewise, while negative correlations were determined between right STN, left PPN values and FOG scores. In neurophysiological assessments, lower performances for visuospatial functions were demonstrated in FOG ( +) individuals for either patient group. The disturbances in the visuospatial abilities may be a critical step for the occurrence of FOG. Together with the results of DTI analyses, it might be suggested that impairment in the connectivity of disturbed frontal areas with disordered basal ganglia, maybe the key factor for the occurrence of FOG in the PD group, whereas left PPN which is a nondopaminergic nucleus may play a more prominent role in the process of FOG in PSP. Moreover, our results support the relationship between right STN, and FOG as mentioned before, as well as introduce the importance of FN as a new structure that may be involved in FOG pathogenesis.

Diffusion Tensor MRI of Brain in Healthy Adult Population: Normative Fractional Anisotropy Values at 3 Tesla MRI

Introduction: Diffusion Tensor Imaging (DTI) technique and its clinical application are increasing in clinical routine practice, still very less normative data is available. Awareness regarding regional differences in Fractional Anisotropy (FA) measurements is very important when routinely DTI is used in clinical Magnetic Resonance Imaging (MRI). Aim: To determine the normative FA values data at 3 Tesla (3T) MRI and to determine the degradation of FA values in various regions of brain values with age. Materials and Methods: This cross-sectional study involved a total of 52 participants without any abnormal findings ( presence of tumour, stroke, infarct, degeneration, etc) and whose brain scanning was performed at 3T MRI, in the Department of Radiodiagnosis, Shree Krishna Hospital, and Pramukhswami Medical College, Karamsad, Anand, Gujarat, India. A DTI protocol was set for the healthy patient’s brain scanning. The colour-coded DTI brain images were postprocessed carefully to draw a circular Region of Interest (ROI) in the required areas of white matter and FA values were noted. Descriptive statistics were used to find out the normative data in 11 regions of the brain on right and left side. Pearson correlation was used to check the correlation of FA values with age. Results: There were 52 patients in the present study, with a male to female ratio of 1.7:1 and a mean age of 52 years. The highest FA values were observed in the splenium (0.809), genu (0.767), the body of the corpus callosum (0.627), and the posterior limb of the internal capsule (0.721), rest areas showed moderate to low FA values. Pearson correlation was used to find the variation in the FA values with age in three age groups 18-40, 41-60 and &gt;60 years, where moderate changes in FA values with age were seen in a few regions of the brain such as genu (right side) with p-value=0.003 and foramen magnum at CVJ level (right side) with a p-value of 0.001. Conclusion: Generally, FA values intend to change with the presence of multiple tract areas, field strength, coil sensitivity, and partial volume averaging. FA values were also found to be affected with respect to increasing age.

Diffusion tensor imaging derived metrics in high grade glioma and brain metastasis differentiation

Background: Pretreatment differentiation between glioblastoma and metastasis is a frequently encountered dilemma in neurosurgical practice. Distinction is required for precise planning of resection or radiotherapy, and also for defining further diagnostic procedures. Morphology and spectroscopy imaging features are not specific and frequently overlap. This limitation of magnetic resonance imaging and magnetic resonance spectroscopy was the reason to initiate this study. The aim of the present study was to determine whether the dataset of diffusion tensor imaging metrics contains information which may be used for the distinction between primary and secondary intra-axial neoplasms. Methods: Two diffusion tensor imaging parameters were measured in 81 patients with an expansive, ring-enhancing, intra-axial lesion on standard magnetic resonance imaging (1.5 T system). All tumors were histologically verified glioblastoma or secondary deposit. For qualitative analysis, two regions of interest were defined: intratumoral and immediate peritumoral region (locations 1 and 2, respectively). Fractional anisotropy and mean difusivity values of both groups were compared. Additional test was performed to determine if there was a significant difference in mean values between two locations. Results: A statistically significant difference was found in fractional anisotropy values among two locations, with decreasing values in the direction of neoplastic infiltration, although such difference was not observed in fractional anisotropy values in the group with secondary tumors. Mean difusivity values did not appear helpful in differentiation between these two entities. In both groups there was no significant difference in mean difusivity values, neither in intratumoral nor in peritumoral location. Conclusion: The results of our study justify associating the diffusion tensor imaging technique to conventional morphologic magnetic resonance imaging as an additional diagnostic tool for the distinction between primary and secondary intra-axial lesions. Quantitative analysis of diffusion tensor imaging metric, in particular measurement of fractional anisotropy in peritumoral edema facilitates accurate diagnosis.

Reproducibility of diffusion tensor imaging-derived parameters: implications for the streptozotocin-induced type 1 diabetic rats.

Diffusion tensor imaging (DTI) is a useful approach for studying neuronal integrity in animals. However, the test-retest reproducibility of DTI techniques in animals has not been discussed. Therefore, the first part of this work was to systematically elucidate the reliability of DTI-derived parameters in an animal study. Subsequently, we applied the DTI approach to an animal model of diabetes in a longitudinal manner. In Study 1, nine rats underwent two DTI sessions using the same scanner and protocols, with a gap of 4weeks. The reliability of the DTI-derived parameters was evaluated in terms of sessions and raters. In Study 2, nine rats received a single intraperitoneal injection of 70mg/kg streptozotocin (STZ) to develop diabetes. Longitudinal DTI scans were used to assess brain alterations before and 4weeks after STZ administration. In the test-retest evaluation, the inter-scan coefficient of variation (CoV) ranged from 3.04 to 3.73% and 2.12-2.59% for fractional anisotropy (FA) and mean diffusivity (MD), respectively, in different brain regions, suggesting excellent reproducibility. Moreover, rater-dependence had minimal effects on FA and MD quantification, with all inter-rater CoV values less than 4%. Following the onset of diabetes, FA in striatum and cortex were noted to be significantly lower relative to the period where they had not developed diabetes (both P < 0.05). However, when compared to the control group, a significant change in FA caused by diabetes was detected only in the striatum (P < 0.05), but not in the cortex. These results demonstrate good inter-rater and inter-scan reliability of DTI in animal studies, and the longitudinal setting has a beneficial effect on detecting small changes in the brain due to diseases.

Diffusion-tensor magnetic resonance imaging of the growth plates: substantiation of the method and evaluation of its reproducibility

INTRODUCTION: In recent years there is an increasing interest in assessing the state of the physis among pediatric orthopedic traumatologists, due to the availability of surgical correction of conditions associated with impaired growth plate function. Application of these methods requires dynamic assessment of the state of the physis. The existing methods for assessing the functional state of the physis are few and imperfect. Magnetic resonance imaging (MRI), in particular diffusion tensor imaging (DTI), is promising for assessing the growth plates.OBJECTIVE: To assess the possibility of applying DTI technique to assess the state of the physis (growth plate).MATERIALS AND METHODS: The study was approved by the local ethical committee of the Institute of the Human Brain named after N.P. Bekhtereva of Russian Academy of Sciences, protocol No. 32, dated 21.12.2020. Informed consent was obtained from each legal representative. At the Turner National Medical Research Center for Сhildren’s Orthopedics and Trauma Surgery of the Ministry of Health of the Russian Federation a standard magnetic resonance examination of the knee joints was performed, additionally diffusion tensor tomography was performed in 75 patients aged 4.1 to 14.3 years (41 males, 34 females), both deemed healthy and with a known physeal pathology. The studies were performed using a Philips Ingenia ElitionX machine with a magnetic field strength of 3 Tesla, DTI was performed in the coronal plane, TE=71.2 ms, TR=4000 ms, b-factor value 600 s/mm2, 15 diffusion directions were used, slice thickness was 3 mm without gaps, the matrix size was 1.25×1.25 mm. The resulting images were processed using the DSI Studio software, including tract construction, estimation of the total volume and average length of the tracts. The reproducibility of the technique was assessed.RESULTS: Higher volumes and mean tract lengths were found in healthy males at puberty compared to other groups. When assessing reproducibility, the standard deviations were about 10% of the final value.DISCUSSION: Images of growth plate tracts were obtained. In individuals of pubertal age that were deemed healthy, the tract volume values and the average length of the tracts were higher than in individuals of prepubertal age and those with pathological conditions affecting the state of the growth plates. The average tract length turned out to be more reproducible than the total tract volume parameter.CONCLUSION: With the help of DTI, it is possible to evaluate the arrangement of the structures in the growth plates.

Advanced pediatric neuroimaging.

Advanced magnetic resonance neuroimaging techniques play an important adjunct role to conventional MRI sequences for better depiction and characterization of a variety of brain disorders. In this article we briefly review the basic principles and clinical utility of a select number of these techniques, including clinical functional MRI for presurgical planning, clinical diffusion tensor imaging and related techniques, dynamic susceptibility contrast perfusion imaging using gadolinium injection, and arterial spin labeling perfusion imaging. The article focuses on general principles of clinical MRI acquisition protocols, relevant factors affecting image quality, and a general framework for obtaining images for each of these techniques. We also present relevant advances for acquiring these types of imaging sequences in a clinical setting.

Diffusion tensor imaging of anisotropic inhomogeneous turbulent flow.

Inhomogeneous anisotropic turbulent flow is difficult to measure, and yet it commonly occurs in nature and in many engineering applications. This work aims to introduce a technique based on magnetic resonance imaging which can spatially map the degree of turbulence as well as the degree of anisotropy. Our interpretation relies on the eddy diffusion model of turbulence, and combines this with the technique of diffusion tensor imaging. The result is an eddy diffusion tensor, which is represented by a symmetric three-by-three matrix. This tensor contains a wealth of information about the magnitude and directions of the turbulent fluctuations; however, the correlation time must be considered before interpreting this information. In the constricted pipe flow used in this study, the turbulence is greatest in magnitude in the space surrounding the core of the turbulent jet, and the turbulence is highly anisotropic.